Model-assisted design of experiments in the presence of network-correlated outcomes

SUMMARY In this paper we consider how to assign treatment in a randomized experiment in which the correlation among the outcomes is informed by a network available pre-intervention. Working within the potential outcome causal framework, we develop a class of models that posit such a correlation stru...

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Bibliographic Details
Published in:Biometrika Vol. 105; no. 4; pp. 849 - 858
Main Authors: Basse, Guillaume W, Airoldi, Edoardo M
Format: Journal Article
Language:English
Published: Oxford University Press 01.12.2018
Subjects:
Causal inference
Rerandomization
Randomized experiment
Network balance
Degree distribution
Network data
Optimal treatment allocation
ISSN:0006-3444, 1464-3510
Online Access:Get full text
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Summary:SUMMARY In this paper we consider how to assign treatment in a randomized experiment in which the correlation among the outcomes is informed by a network available pre-intervention. Working within the potential outcome causal framework, we develop a class of models that posit such a correlation structure among the outcomes. We use these models to develop restricted randomization strategies for allocating treatment optimally, by minimizing the mean squared error of the estimated average treatment effect. Analytical decompositions of the mean squared error, due both to the model and to the randomization distribution, provide insights into aspects of the optimal designs. In particular, the analysis suggests new notions of balance based on specific network quantities, in addition to classical covariate balance. The resulting balanced optimal restricted randomization strategies are still design-unbiased when the model used to derive them does not hold. We illustrate how the proposed treatment allocation strategies improve on allocations that ignore the network structure.
ISSN:0006-3444
1464-3510
DOI:10.1093/biomet/asy036